employ: Employ a Clustering to New Data

In tame: Timing, Anatomical, Therapeutic and Chemical Based Medication Clustering

Employ a Clustering to New Data

Description

Employ a clustering to new data

Usage

employ(
  object,
  new_data,
  only = NULL,
  additional_data = NULL,
  assignment_method = "nearest_cluster",
  parallel = FALSE,
  ...
)

Arguments

object	A medic clustering object for which employment is desired.
new_data	A data frame in which to look for variables with
only	<data-masking> Expressions that return a logical value, and are defined in terms of the variables in object and/or additional_data and specifies which clusterings should be employed to the new data.
additional_data	A data frame with additional data that may be (left-)joined onto the parameters in object. This is often used in conjuction with only to select specific clusterings based on additional_data.
assignment_method	A character naming the employment method. The default assignment method "nearest_cluster" matches people in new_data to their nearest cluster in the chosen clusterings from object. As finding exact matches (the next assignment method) is contained within this strategy the "exact_only" matches are also reported in additional columns in the output. The assignment method "exact_only" only matches a person from new_data to a cluster if they are a perfect match to anyone in object. Thus, people from new_data are not guaranteed assignment to a cluster.
parallel	A logical or an integer. If FALSE, the default, no parallelization is done. If TRUE or an integer larger than 2L parallelization is implemented via parLapply from the parallel package. When parallel is TRUE the number of clusters is set to detectCores - 1, and when parallel is an integer then the number of clusters is set to parallel. For more details on the parallelization method see parallel::parLapply.
...	Additional arguments affecting the employment procedure.

Value

employ returns a medic object.

Examples

part1 <- complications[1:100,]
part2 <- complications[101:149,]

clust <- medic(part1, id = id, atc = atc, k = 3)

# Nearest cluster matching
employ(clust, part2)

# Only exact matching
employ(clust, part2, assignment_method = "exact_only")

tame documentation built on April 12, 2025, 1:40 a.m.